news-agent (AgentEra/Agently-Daily-News-Collector) | MoltPulse

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news-agent

An open-source LLM based automatically daily news collecting workflow showcase powered by Agently AI application development framework.

AgentEra/Agently-Daily-News-Collector00

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README.md

Agently Daily News Collector v4

Agently Daily News Collector has been rewritten on top of Agently v4 and now uses:

TriggerFlow for the end-to-end pipeline
Agently v4 built-in Search and Browse tools
structured output contracts instead of the old v3 workflow API

Version constraint: this project requires Agently v4.0.8.3 or newer. The current implementation uses TriggerFlow sub flow to organize per-column pipelines, so earlier v4 releases are not compatible with the workflow structure used here.

The previous Agently v3 project has been archived under ./v3.

Features

Input a topic and generate a multi-column news briefing automatically
Search, shortlist, browse, summarize, and assemble stories in one flow
Save the final report as Markdown under ./outputs
Keep prompt templates in ./prompts for easy editing
Keep an independent ./tools layer so search/browse can be replaced without touching the main workflow
Keep flow construction in ./workflow so orchestration can evolve independently from collector logic

Quick Start

Install dependencies:

pip install -r requirements.txt

If you install Agently manually, make sure you use at least:

pip install "agently>=4.0.8.3"

Edit SETTINGS.yaml:

Keep the model block as environment placeholders
Export the required environment variables:

export AGENTLY_NEWS_BASE_URL="https://api.openai.com/v1"
export AGENTLY_NEWS_MODEL="gpt-4.1-mini"
export AGENTLY_NEWS_API_KEY="your_api_key"

AGENTLY_NEWS_BASE_URL=https://api.openai.com/v1
AGENTLY_NEWS_MODEL=gpt-4.1-mini
AGENTLY_NEWS_API_KEY=your_api_key

python app.py

python app.py "AI agents"

.
├── app.py
├── news_collector/
├── tools/
├── workflow/
├── prompts/
├── outputs/
├── logs/
└── v3/

TriggerFlow orchestration
- Replaces the old v3 workflow style with a more explicit flow graph (to, for_each, sub flow, branching-ready composition).
- Unlike the old v3 Workflow chain, TriggerFlow here runs columns concurrently and also summarizes picked stories concurrently within each column.
- Meaning for this project: the end-to-end news pipeline is easier to inspect, evolve, and split into chunks without mixing orchestration with business logic, while the parent report flow and the per-column pipeline can now be modeled directly as parent/child flows instead of one oversized chunk.
Sub flow composition
- The project can now extract a naturally repeated business pipeline, “build one column”, into its own TriggerFlow and invoke it repeatedly from the parent flow inside for_each(column).
- Meaning for this project:
  - the parent flow stays focused on report-level orchestration
  - the column pipeline can be tested, visualized, and exported independently
  - future variants such as “briefing column”, “deep-dive column”, or “regional column” can reuse or derive from the child flow instead of cloning parent-flow nodes
  - capture / write_back makes the boundary between parent and child explicit for input, state, and resources
Structured output contracts
- YAML prompts now define output schema directly for outline generation, news picking, summarizing, and column writing.
- Meaning for this project: much less handwritten parsing glue, clearer interfaces between steps, and easier prompt iteration.
Built-in Search / Browse tools
- The project now defaults to Agently v4 built-in tool implementations instead of the old project-local helpers.
- Meaning for this project: less custom infrastructure code, and users can still swap implementations through ./tools without rewriting the workflow.
Runtime resources and state namespaces
- TriggerFlow runtime resources are used to inject logger/search/browse dependencies, while runtime state stores execution data such as request, outline, and intermediate results.
- Meaning for this project: dependency wiring and execution state are separated cleanly, which keeps chunk code thinner and more maintainable.
Environment-aware settings
- Agently v4 set_settings(..., auto_load_env=True) works directly with ${ENV.xxx} placeholders.
- Meaning for this project: model endpoint, model name, and API key can be switched by environment instead of editing code or committing secrets.